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In Silico-Identified miR-16-5p and miR-32-5p as a Shared Molecular Signature of Primary Gliomas and Parkinson's Disease: Plasma Levels Are Increased Only in Glioma Patients.

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  1. [1] § 2. Methods › 2.1. Bioinformatic Assessment ↔ vignettes/vignette_wizbionet.Rmd, lines 413 to 447 · score 0.55 · multiMiR, target prediction, target interactions, miRNAs, genes · Report a mismatch

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  1. ---
  2. title: "vignette-wizbionet"
  3. author: "Zofia Wicik"
  4. date: "2026-07-21 (wizbionet 1.3.0 update)"
  5. output: rmarkdown::html_vignette
  6. vignette: >
  7. %\VignetteIndexEntry{vignette-wizbionet}
  8. %\VignetteEngine{knitr::rmarkdown}
  9. %\VignetteEncoding{UTF-8}
  10. ---
  11. <!-- .markdown-block { -->
  12. <!-- padding-top: 0 !important; -->
  13. <!-- padding-bottom: 0 !important; -->
  14. <!-- } -->
  15. ```{r setup, include = FALSE}
  16. knitr::opts_chunk$set(
  17. collapse = TRUE,
  18. comment = "#>"
  20. )
  21. ```
  23. ## Introduction
  24. This package was build to provide set of tools enabling identification of the top genes and non-coding RNAs (miRNAs, lncRNAs) from expression studies. It can also be applied to arbitrary selected lists of genes mined from the literature.
  26. In order to identify best coding and non-coding genes that would serve as key regulators of complex diseases, we developed a key-gene scoring system taking into account their mutual regulation. In this document we we will try to show examples of analyses which can be made with this toolbox:
  28. ## Installation
  29. ```{r eval=FALSE, fig.width=10}
  30. #Some of the dependencies are not downloaded automatically yet.
  31. #Below is the code which will install all of them. Just copy it and paste to your R console
  32. #dependencies
  33. #dplyr (>= 1.0.2), multiMiR (>= 1.8.0), stringr (>= 1.4.0), XML (>= 3.99-0.5),
  34. #OneR (>= 2.2.0), plyr (>= 1.8.6),
  35. #tidyselect (>= 1.1.0), kableExtra (>= 1.1.0), knitr (>= 1.29), rmarkdown (>= 2.3), utils
  38. # 1. installation of the necessary packages, if it will not work download source file from the webpage.
  39. #https://www.bioconductor.org/packages/release/bioc/html/multiMiR.html
  41. if (!requireNamespace("BiocManager", quietly=TRUE))
  42. install.packages("BiocManager")
  43. BiocManager::install()
  44. BiocManager::install("multiMiR")
  46. # https://cran.r-project.org/web/packages/OneR/index.html
  47. install.packages("OneR", type = "binary")
  49. #https://cran.r-project.org/web/packages/kableExtra/
  50. install.packages('kableExtra', type = "binary")
  53. # 2 A. You can install package wizbionet from the GitHub use this command:
  54. install.packages("remotes")
  55. remotes::install_github("wizbionet/wizbionet", dependencies ="Depends")
  57. # 2 B.Or install wizbionet from the wizbionet_0.99.0.tar.gz file downloaded from this link:
  58. #https://github.com/wizbionet/wizbionet/raw/master/wizbionet_0.99.0.tar.gz
  60. install.packages("https://github.com/wizbionet/wizbionet/raw/master/wizbionet_0.99.0.tar.gz",
  61. repos = NULL, type = "source",
  62. dependencies =TRUE
  63. )
  65. #Voila, load the package:
  66. library(wizbionet)
  67. ```
  70. ## wizbionet workflow example
  73. ### 1. importing gene lists to the R
  75. This set of functions enables combining gene lists associated with your process of interest. You can copy columns from excel and paste them to R using command:
  77. ```{r, eval= FALSE }
  78. # genelistGWAS<- readClipboard()
  79. # genelistGO<- readClipboard()
  80. # genelistKEGG<- readClipboard()
  81. ```
  85. ### 2. Updating gene symbols annotation using NCBI_synonyms()
  87. The common problem while working with different gene lists are not matching gene symbols which can generates duplicates while merging them. this package offers tool for efficient annotation of human genes according to NCBI nomenclature using function **NCBI_synonyms()**. Notice that gene EP3 has now symbol PTGER3, and SNT3 has symbol SNTB2.
  89. ```{r, eval= FALSE }
  90. genelistGWAS<- as.data.frame(genelistGWAS)
  91. NCBI_synonyms(genelistGWAS, "genelistGWAS")
  92. ```
  93. ```{r, echo=FALSE }
  95. invisible(capture.output(dat1<- data.frame(genelistGWAS=c('SGCD', 'TBC1D8B', 'EP3', 'ANO5', 'FGFR1', 'PTGFR','SNT3', 'USP9Y', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))))
  96. invisible(capture.output(output<- wizbionet::NCBI_synonyms(dat1, 'genelistGWAS')))
  98. temp<-kableExtra::kable_styling(knitr::kable(output, align="l"),font_size=9, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", stripe_color = "white")
  99. temp<-kableExtra::column_spec(temp, column=2, color='red')
  100. temp
  102. ```
  104. ### 3. Selecting columns based on syntax using col_selector()
  106. If you are not interested in other columns except for the official gene symbols you can use syntax specific function:
  108. ```{r, eval= FALSE }
  109. col_selector(genelistGWAS, key_words = "SymbolNCBI")
  110. #Use Regular Expression Syntax (https://rstudio.com/wp-content/uploads/2016/09/RegExCheatsheet.pdf) to precisely select columns:
  111. #Meta characters . * +
  112. #Anchors: ^ Start of the string; $ End of the string
  113. ```
  116. ### 4. Binding multiple gene lists and/or data.frames of unequal lenght using cbind_filler()
  118. To be able to bind multiple gene lists and/or data.frames of unequal lenght u can use **cbind_filler()**
  119. ```{r, eval= FALSE }
  120. output<- cbind_filler(list(genelistGWAS,genelistGO,genelistKEEG))
  121. ```
  122. If you are working with vectors after binding them you will need to add names manually. If the gene lists were data frames with headings you don't need to do this.
  123. ```{r, eval= FALSE }
  124. names(output)<-c('genelistGWAS','genelistGO','genelistKEEG')
  125. ```
  128. Below is example data frame merged using **cbind_filler()** function with suggestions of the sources of gene lists. The advantage of this function is that it allows to combine vectors/data frames of different lenght
  129. ```{r, eval= FALSE }
  130. output<- cbind_filler(output)
  131. ```
  133. ```{r, echo=FALSE }
  135. dat1<- data.frame(genelistGWAS=c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR','SNTB2', 'USP9Y', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))
  136. dat2<- data.frame(genelistGO=c('CDH24', 'ONECUT2', 'NEBL', 'KAT6A', 'PTGER3', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))
  137. dat3<- data.frame(genelistKEGG=c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR', 'NELL1', 'NR2C2', 'TGFB3', 'BRD1', 'RAD23B', 'PRKDC', 'IGSF10', 'AKIRIN1', 'HACD4', 'C4orf32'))
  138. input<- list(dat1,dat2,dat3)
  140. invisible(capture.output(output<- wizbionet::cbind_filler(input)))
  141. rownames(output)<-NULL
  145. kableExtra::kable_styling(knitr::kable(output, align="l"),font_size=9, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", stripe_color = "white",
  146. )
  147. ```
  149. ### 5. Combining and summarizing multiple gene lists using lists.combiner()
  150. When you have already a data frame build using **cbind_filler()** or just imported form txt/excel or csv file, we suggest to use function **lists.combiner()**.
  151. This function will merge all of those data frames and will provide information in which list a given gene was present and in how many lists in total it appeared.
  153. ```{r, eval= FALSE }
  154. output<- lists.combiner(output)
  155. head(output, n=15)
  156. ```
  158. ```{r, echo=FALSE }
  159. dat1<- data.frame(genelistGWAS=c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR','SNTB2', 'USP9Y', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))
  160. dat2<- data.frame(genelistGO=c('CDH24', 'ONECUT2', 'NEBL', 'KAT6A', 'PTGER3', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))
  161. dat3<- data.frame(genelistKEGG=c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR', 'NELL1', 'NR2C2', 'TGFB3', 'BRD1', 'RAD23B', 'PRKDC', 'IGSF10', 'AKIRIN1', 'HACD4', 'C4orf32'))
  162. input<- list(dat1,dat2,dat3)
  164. invisible(capture.output(temp<- wizbionet::cbind_filler(input)))
  165. invisible(capture.output(output<- wizbionet::lists.combiner(temp)))
  166. rownames(output)<-NULL
  167. temp<-kableExtra::kable_styling(knitr::kable(head(output,n=15)),font_size=9, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", stripe_color = "white",
  168. )
  169. temp<-kableExtra::column_spec(temp, column=5, color='red')
  171. temp
  172. ```
  174. Now you have a complete list of genes of interest (column input) and can easily see that gene PTGER3 was present in all of those data sets. You can also use function **NCBI_synonyms()** to add gene IDs.
  175. ```{r, eval= FALSE }
  176. output<- NCBI_synonyms(output, "input")
  177. head(output, n=10)
  178. ```
  182. ```{r, echo=FALSE }
  183. dat1<- data.frame(genelistGWAS=c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR','SNTB2', 'USP9Y', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))
  184. dat2<- data.frame(genelistGO=c('CDH24', 'ONECUT2', 'NEBL', 'KAT6A', 'PTGER3', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))
  185. dat3<- data.frame(genelistKEGG=c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR', 'NELL1', 'NR2C2', 'TGFB3', 'BRD1', 'RAD23B', 'PRKDC', 'IGSF10', 'AKIRIN1', 'HACD4', 'C4orf32'))
  186. input<- list(dat1,dat2,dat3)
  188. invisible(capture.output(temp<- wizbionet::cbind_filler(input)))
  189. invisible(capture.output(output<- wizbionet::lists.combiner(temp)))
  190. invisible(capture.output(output<- wizbionet::NCBI_synonyms(output, "input")))
  191. #head(output, n=15)
  193. temp<-kableExtra::kable_styling(knitr::kable(head(output,n=10)),font_size=9, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", stripe_color = "white",
  194. )
  195. temp<-kableExtra::column_spec(temp, column=c(6,7,8), color='red')
  196. temp
  197. ```
  199. ### 6. Clusterization of gene lists using clusterizer_oneR()
  202. If you want to know genes associated with highest number of genes lists you can use function **clusterizer_oneR()**, which will divide genes from the "input" column into four clusters based on column "count". clusterizer_oneR() utilzes Jenks natural breaks optimization algorithm from the original OneR package as a non-arbitrary classification dividing numbers of regulated genes into four categories (clusters). advantage of this method is non-arbitrary selection of top hits. You can cluster multiple numeric columns using this function. If you want to change the number of bins or method of the clusterization you can use function OneR::bin
  203. ```{r, eval= FALSE }
  204. landmark_col,="SymbolNCBI"
  205. cols_to_cluster="count"
  206. output<- clusterizer_oneR(output, landmark_col, cols_to_cluster)
  207. head(output, n=10)
  208. ```
  211. ```{r, echo=FALSE }
  212. dat1<- data.frame(genelistGWAS=c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR','SNTB2', 'USP9Y', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))
  213. dat2<- data.frame(genelistGO=c('CDH24', 'ONECUT2', 'NEBL', 'KAT6A', 'PTGER3', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))
  214. dat3<- data.frame(genelistKEGG=c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR', 'NELL1', 'NR2C2', 'TGFB3', 'BRD1', 'RAD23B', 'PRKDC', 'IGSF10', 'AKIRIN1', 'HACD4', 'C4orf32'))
  215. input<- list(dat1,dat2,dat3)
  217. invisible(capture.output(temp<- wizbionet::cbind_filler(input)))
  218. invisible(capture.output(output<- wizbionet::lists.combiner(temp)))
  219. invisible(capture.output(output<- wizbionet::NCBI_synonyms(output, "input")))
  221. invisible(capture.output(output<- wizbionet::clusterizer_oneR(output, landmark_col="SymbolNCBI", cols_to_cluster="count")
  222. ))
  223. output<-as.data.frame( output)
  224. temp<-kableExtra::kable_styling(knitr::kable(head(output,n=15)),font_size=9, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", )
  225. temp<-kableExtra::column_spec(temp, column=c(9,10), color='red')
  226. temp
  227. ```
  229. ### 7. Clusterization of gene lists using top_percent()
  232. top_percent() is an alternative for **clusterizer_oneR()** which doesn't deal well with continuous numbers like p-values or fold changes. This function enables comparison of data sets of different length.
  233. It is suggested to use it on gene lists which have associated numeric values.
  234. Prioritization of the analyzed gene lists can be based on the scores assigned after data aggregation and counting.
  235. This function helps to avoid arbitrary selection of top candidates, selecting top percent of genes for a given cutoff. It includes all genes close to a cutoff if they have same value. It generates new column with TRUE or FALSE value giving information if our gene was present in the top percents.
  237. ```{r, eval= FALSE }
  238. landmark_col,="SymbolNCBI"
  239. cols_to_cluster="count"
  240. cutoff=25
  241. output<- clusterizer_oneR(output, landmark_col, cols_to_cluster, cutoff)
  242. head(output, n=10)
  243. ```
  246. ```{r, echo=FALSE }
  247. dat1<- data.frame(genelistGWAS=c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR','SNTB2', 'USP9Y', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))
  248. dat2<- data.frame(genelistGO=c('CDH24', 'ONECUT2', 'NEBL', 'KAT6A', 'PTGER3', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))
  249. dat3<- data.frame(genelistKEGG=c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR', 'NELL1', 'NR2C2', 'TGFB3', 'BRD1', 'RAD23B', 'PRKDC', 'IGSF10', 'AKIRIN1', 'HACD4', 'C4orf32'))
  250. input<- list(dat1,dat2,dat3)
  252. invisible(capture.output(temp<- wizbionet::cbind_filler(input)))
  253. invisible(capture.output(output<- wizbionet::lists.combiner(temp)))
  254. invisible(capture.output(output<- wizbionet::NCBI_synonyms(output, "input")))
  255. invisible(capture.output(output<- wizbionet::top_percent(output, landmark_col="SymbolNCBI", cols_to_cluster="count", cutoff=25 )))
  256. output<-as.data.frame( output)
  257. temp<-kableExtra::kable_styling(knitr::kable(head(output,n=15)),font_size=9, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", )
  258. temp<-kableExtra::column_spec(temp, column=c(9), color='red')
  259. temp
  260. ```
  263. ### 8. Merging of the multiple data frames with headings preservation using dataframe.merger()
  264. As you can see in the output appeared two columns 'clus_count' and 'clusNR_count'. First column provides logical information if gene was present in the top two clusters (cl1, cl2). Second column provides information in which cluster appeared this gene.
  265. You can combine this data frame with your another data frame prioritized in same way using common column (in this case "SymbolNCBI") and function **dataframe.merger()**.
  266. ```{r, eval= FALSE }
  267. dataframe_list=list('output'=output, 'other_dataframe'=other_dataframe)
  268. ID_column="input"
  269. dataframe.merger(dataframe_list, ID_column="input")
  270. ```
  271. If you are analyzing two data sets using this approach you can select genes present in both of them, so called top genes. This approach enables quick selection of top candidates across multiple datasates.
  273. ```{r, eval= FALSE }
  274. landmark_col="SymbolNCBI"
  275. cols_to_cluster= c("count", 'count_regulatory_miRNA')
  276. output<- clusterizer_oneR(output, landmark_col, cols_to_cluster)
  277. head(output, n=10)
  278. ```
  280. ```{r, echo=FALSE }
  282. dat1<- data.frame(genelistGWAS=c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR','SNTB2', 'USP9Y', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))
  283. dat2<- data.frame(genelistGO=c('CDH24', 'ONECUT2', 'NEBL', 'KAT6A', 'PTGER3', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))
  284. dat3<- data.frame(genelistKEGG=c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR', 'NELL1', 'NR2C2', 'TGFB3', 'BRD1', 'RAD23B', 'PRKDC', 'IGSF10', 'AKIRIN1', 'HACD4', 'C4orf32'))
  285. input<- list(dat1,dat2,dat3)
  287. invisible(capture.output(temp<- wizbionet::cbind_filler(input)))
  288. invisible(capture.output(output<- wizbionet::lists.combiner(temp)))
  289. invisible(capture.output(output<- wizbionet::NCBI_synonyms(output, "input")))
  290. output<-output[,c("SymbolNCBI", "count")]
  291. rownames(output)<-NULL
  292. output$count_regulatory_miRNA<- rnorm(n=24, m=65, s=30)
  293. output$count_regulatory_miRNA<- round(output$count_regulatory_miRNA)
  294. landmark_col="SymbolNCBI"
  295. cols_to_cluster= c("count", 'count_regulatory_miRNA')
  296. invisible(capture.output(output<- wizbionet::clusterizer_oneR(output, landmark_col, cols_to_cluster)))
  298. temp<-kableExtra::kable_styling(knitr::kable(head(output,n=15)),font_size=9, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", stripe_color = "white", )
  299. temp<-kableExtra::column_spec(temp, column=c(4,6), color='red')
  300. temp
  304. ```
  306. ### 9. Aggregation and summarizing of the rows across multiple columns using col_agrecounter()
  308. Function ***col_agrecounter*()*** allows to aggregates and summarize complex data frames with preserving names of the columns and providing:
  309. - aggregated occurrences for example genes or miRNAs in a column with preserved name and suffix "_coll"
  310. - count of occurrences from column "_coll" as collumn with suffix "_count". After this function we suggest runing top_percent() or clusterizer_oneR() to see which miRNAs are the most interesting ones based on the number of regulated targets.
  312. ```{r, eval= FALSE }
  313. #Example
  314. dat1<- data.frame(
  315. mature_miRNA=c('hsa-miR-195-5p', 'hsa-miR-195-3p','hsa-miR-195-5p', 'hsa-miR-195-5p',
  316. 'hsa-miR-4753-5p', 'hsa-miR-4753-3p'),
  317. pre_miRNA=c('hsa-miR-195', 'hsa-miR-195','hsa-miR-195', 'hsa-miR-195',
  318. 'hsa-miR-4753', 'hsa-miR-4753'),
  319. Target=c('CDH24', 'PAX1', 'PTGER3', 'ONECUT2', 'TGFB3', 'FGFR1'))
  322. #set parameters####
  324. #dataframe for aggegation and counting (A&C)
  325. inputDF<-dat1
  326. #selected column names which will be A&C
  327. col_names <- c( "Target")
  328. #landmark column for A&C columns,other columns will be aggregated based on this
  329. col_collapse <- "pre_miRNA"
  330. #vector of content of the landmark column "col_collapse" as vector.
  331. #You can use internal col_to_string() function
  333. rows_collapse <-col_to_string(inputDF$pre_miRNA)
  334. #additional landmark column which will not allow to deduplicate. useful when you want to analyze pre-miRNAs instead of mature mirNAs
  335. control_col<- "mature_miRNA"
  337. #run function
  338. output<- col_agrecounter(inputDF, col_names, col_collapse , rows_collapse, control_col)
  339. ```
  341. ```{r, echo= FALSE }
  342. #Example
  343. dat1<- data.frame(
  344. mature_miRNA=c('hsa-miR-195-5p', 'hsa-miR-195-3p','hsa-miR-195-5p', 'hsa-miR-195-5p',
  345. 'hsa-miR-4753-5p', 'hsa-miR-4753-3p'),
  346. pre_miRNA=c('hsa-miR-195', 'hsa-miR-195','hsa-miR-195', 'hsa-miR-195',
  347. 'hsa-miR-4753', 'hsa-miR-4753'),
  348. Target=c('CDH24', 'PAX1', 'PTGER3', 'ONECUT2', 'TGFB3', 'FGFR1'))
  351. #set parameters####
  353. #dataframe for aggegation and counting (A&C)
  354. inputDF<-dat1
  355. #selected column names which will be A&C
  356. col_names <- c( "Target")
  357. #landmark column for A&C columns,other columns will be aggregated based on this
  358. col_collapse <- "pre_miRNA"
  359. #vector of content of the landmark column "col_collapse" as vector.
  360. #You can use internal col_to_string() function
  362. rows_collapse <-wizbionet::col_to_string(inputDF$pre_miRNA)
  363. #additional landmark column which will not allow to deduplicate. useful when you want to analyze pre-miRNAs instead of mature mirNAs
  364. control_col<- "mature_miRNA"
  366. #run function
  367. output<- wizbionet::col_agrecounter(inputDF, col_names, col_collapse , rows_collapse, control_col)
  368. #names(output)
  369. temp<-kableExtra::kable_styling(knitr::kable(head(output,n=15)),font_size=9, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", stripe_color = "white", )
  370. temp<-kableExtra::column_spec(temp, column=c(6,7), color='red')
  371. temp
  372. ```
  374. ### 10. Identification of the top genes across datasets
  376. Using base::subset function you can extract top genes which got high scores in both columns in this case "count" and 'count_regulatory_miRNA'.
  382. ```{r, eval= FALSE }
  383. output<-subset(output, output$clus_count==TRUE & output$clus_count_regulatory_miRNA==TRUE )
  384. ```
  385. Example data frame:
  386. ```{r, echo=FALSE }
  388. dat1<- data.frame(genelistGWAS=c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR','SNTB2', 'USP9Y', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))
  389. dat2<- data.frame(genelistGO=c('CDH24', 'ONECUT2', 'NEBL', 'KAT6A', 'PTGER3', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC'))
  390. dat3<- data.frame(genelistKEGG=c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR', 'NELL1', 'NR2C2', 'TGFB3', 'BRD1', 'RAD23B', 'PRKDC', 'IGSF10', 'AKIRIN1', 'HACD4', 'C4orf32'))
  391. input<- list(dat1,dat2,dat3)
  392. invisible(capture.output(temp<- wizbionet::cbind_filler(input)))
  393. invisible(capture.output(output<- wizbionet::lists.combiner(temp)))
  394. invisible(capture.output(output<- wizbionet::NCBI_synonyms(output, "input")))
  395. output<-output[,c("SymbolNCBI", "count")]
  397. output$count_regulatory_miRNA<- rnorm(n=24, m=65, s=30)
  398. output$count_regulatory_miRNA<- round(output$count_regulatory_miRNA)
  399. landmark_col="SymbolNCBI"
  400. cols_to_cluster= c("count", 'count_regulatory_miRNA')
  401. invisible(capture.output(output<- wizbionet::clusterizer_oneR(output, landmark_col, cols_to_cluster)))
  402. output<-subset(output, output$clus_count==TRUE & output$clus_count_regulatory_miRNA==TRUE )
  403. rownames(output)<-NULL
  406. temp<-kableExtra::kable_styling(knitr::kable(head(output,n=15)),font_size=9, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", stripe_color = "white", )
  407. temp<-kableExtra::column_spec(temp, column=c(4,6), color='red')
  408. temp
  409. ```
  413. ### 11. Identification of the top miRNAs and top targets using topmiRNA_toptarget()###
  415. As top miRNAs we define miRNAs associated with your process of interest and regulating highest number of deferentially expressed genes (DE genes). If you have expression data for miRNAs and mRNAs you can perform target predictions to identify pairs of oppositely regulated miRNAs and genes use function **topmiRNA_toptarget()**. This function retrieves miRNA-target interactions and identify miRNAs and genes with highest number of analyzed interactions with miRNAs. You can also use only targets associated with a specific process to identify top miRNAs regulating it.
  417. ```{r topmiRNA_toptarget, eval= FALSE }
  418. #generate and modify list of arguments. Important: Don't add mirna= and target= fields they are already included as DEmir_up,DEgenes_down,DEmir_down, DEgenes_up!
  419. multimir_args<- as.list(args(multiMiR::get_multimir))
  421. #add lists of DE miRNAs and genes
  422. DEmir_up<-c('hsa-miR-150-5p','hsa-miR-448-5p','hsa-miR-448-3p',
  423. 'hsa-miR-493-5p','hsa-miR-493-3p') # example DE miRNAs
  424. DEgenes_down<-c('5797','8826','7994','2775','7182','79647','5733',
  425. '158158','9480','8626','50636') # example DE genes
  426. DEmir_down<-c('hsa-miR-4731-5p','hsa-miR-541-3p','hsa-miR-449b-5p','hsa-miR-541-5p')
  427. DEgenes_up<-c('203859','4745','4916','126298','2258','8464','55917','23450','29767')
  428. mirna_type<-"pre_mir" # "mature_mir"
  429. multimir_args= list(url = NULL,
  430. org = "hsa",
  431. table = "all",
  432. predicted.cutoff = 10,
  433. predicted.cutoff.type = "p",
  434. predicted.site = "conserved"
  435. )
  437. #execute function
  438. output<- topmiRNA_toptarget(DEmir_up,DEgenes_down, DEmir_down, DEgenes_up, multimir_args,mirna_type)
  441. #execute function
  442. output<-wizbionet::topmiRNA_toptarget(DEmir_up,DEgenes_down,DEmir_down,
  443. DEgenes_up, mir_type, multimir_args)
  444. output$multimir_output
  445. output$top_miR
  446. output$top_gene
  447. ```
  450. #### Output of the **topmiRNA_toptarget()** is a list() with three data frames named:
  453. ##### **A. multimir_output**
  455. Data frame *multimir_output* is based on original output from the multiMiR::get_multimir(), but integrates two separate analyses for up and down regulated miRNAs. Directionality of expression is shown in the column "multimir". Additionally, at the end of the dataframe is added column "premir" where are present names of analyzed miRNAs before maturation.
  458. ```{r, echo=FALSE}
  459. utils::data(topmiRNA_toptarget_output, package = 'wizbionet')
  460. output<-topmiRNA_toptarget_output$multimir_output
  461. output<-head(output,n=15)
  463. #table
  464. temp<- knitr::kable(output, align="l")
  465. temp<- kableExtra::kable_styling(temp,font_size=8, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", stripe_color = "white")
  466. temp<-kableExtra::column_spec(temp, column=c(14,15),color="red")
  467. temp<-kableExtra::scroll_box(temp, width = "100%", height = "200px")
  469. temp
  471. ```
  475. ##### **B. top_miR**
  477. Data frame *top_miR* has aggregated and prioritized results from get_multimir output showing number of genes associated with pre-miRNAs. It has columns with name *clus_...* providing logical information if gene was in top 2 clusters (cl1 and cl2) ~top 20 percents and column *clusNR_..*. providing information in which cluster the gene was present (cl1,cl2,cl3,cl4). It enables quick identification of the miRNAs regulating highest number of analyzed targets
  479. ```{r topmiRNA_toptarget: top_miR, echo=FALSE }
  480. utils::data(topmiRNA_toptarget_output, package = 'wizbionet')
  481. output<-topmiRNA_toptarget_output$top_miR
  482. output<-head(output,n=15)
  483. rownames(output)<-NULL
  484. #names(output)
  485. #table
  486. temp<- knitr::kable(output, align="l")
  487. temp<- kableExtra::kable_styling(temp,font_size=8, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", stripe_color = "white")
  488. temp<-kableExtra::column_spec(temp, column=c(6,7,12,13), color = "red")
  489. temp<-kableExtra::scroll_box(temp, width = "100%", height = "200px")
  491. temp
  493. ```
  496. ##### **C. top_gene**
  498. Data frame *top_gene* has aggregated and prioritized results from get_multimir function showing number of genes associated with analyzed targets. It also has columns with information if gene is in top 20%.
  501. ```{r topmiRNA_toptarget: top_gene, echo=FALSE }
  502. utils::data(topmiRNA_toptarget_output, package = 'wizbionet')
  503. output<-topmiRNA_toptarget_output$top_gene
  504. output<-head(output,n=10)
  505. rownames(output)<-NULL
  506. #names(output)
  507. #table
  508. temp<- knitr::kable(output, align="l")
  509. temp<- kableExtra::kable_styling(temp,font_size=8, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", stripe_color = "white")
  510. temp<-kableExtra::column_spec(temp, column=c(4,5,6,8), color="red")
  511. temp<- kableExtra::scroll_box(temp, width = "100%", height = "200px")
  513. temp
  515. ```
  516. ### 12. Identification of the top long-non coding RNAs and their targets using lnc_RNARNA_scanner()
  519. Function ***lnc_RNARNA_scanner*** identify targets of lncRNAs by performing automatized mining of the [link](http://rtools.cbrc.jp/cgi-bin/RNARNA/index.pl) database using ENST transcript IDs (ENST_input) as an input. It enables identification of so-called top lncRNAs associated with your process of interest and regulating highest number of deferentially expressed genes (DE genes)
  520. **ENST_input** is a vector of characters (human ENST transcript numbers). RNARNAdb will show information during the analysis if non coding transcript was present in the database. Careful, it sometimes also recognizes miRNAs.
  522. **analyze_onlyDEtargets** TRUE or FALSE (logical), it says if you want to check all possible lncRNA targets or only DE targets.
  524. **ENST_targets** vector of characters of human ENST numbers of your interest, usually DE mRNAs
  526. **nr_top_genes** is a number number of top targets you want to select from all possible targets of lncRNAs. Default value for RNARNA database top targets output set by us is 500 if you want to compare it with DE genes. Your ENST_targets will be subtracted from this number.
  529. ```{r, eval= FALSE }
  530. #Example of the analysis
  531. ENST_input<-c('ENST00000436290', 'ENST00000432892', 'ENST00000454526',
  532. 'ENST00000423793', 'ENST00000436960','ENST00000412754'
  533. )
  535. ENST_targets<-c('ENST00000491143', 'ENST00000314191',
  536. 'ENST00000309733', 'ENST00000377122', 'ENST00000338981',
  537. 'ENST00000495827', 'ENST00000425708', 'ENST00000272748',
  538. ...)
  539. nr_top_genes=500
  540. analyze_onlyDEtargets=FALSE
  541. output<- lnc_RNARNA_scanner(ENST_input, analyze_onlyDEtargets,
  542. ENST_targets ,nr_top_genes)
  543. names(output)
  546. ```
  548. #### lnc_RNARNA_scanner output
  550. Output of **lnc_RNARNA_scanner** function is a list of data frames. To access the lists use $ sign or[[]].
  551. All outputs have column ""full_id" which describes interacting RNAs with your lncRNAs and provide a full name of the lncRNA for example: "List of RNAs interacting with ENST00000423793(RP11-432J22.2)". You can use this column for aggregating and summarizing those datasets using **col_agrecounter()** and than **clusterizer_oneR()**
  552. to obtain top lncRNAs or top targets.
  554. ##### **A. if analyze_onlyDEtargets= was set as TRUE**
  556. The output will include list *"lncRNA_DEtargets"* which has only top DE targets of analyzed lncRNAs
  557. ```{r lnc_RNARNA_scanner: analyze_onlyDEtargets_TRUE, echo=FALSE }
  558. utils::data(lnc_RNARNA_scanner_output, package = 'wizbionet')
  559. output<-lnc_RNARNA_scanner_output$lncRNA_DEtargets
  560. output<-head(output,n=10)
  561. rownames(output)<-NULL
  562. #names(output)
  563. #table
  564. temp<- knitr::kable(output, align="l")
  565. temp<- kableExtra::kable_styling(temp,font_size=8, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", stripe_color = "white")
  566. temp<-kableExtra::column_spec(temp, column=c(10), color="red")
  567. temp<- kableExtra::scroll_box(temp, width = "100%", height = "200px")
  569. temp
  571. ```
  575. ##### **B. if analyze_onlyDEtargets= was set as FALSE**
  577. The output will include list *"lncRNA_AllTargets"* which has all possible top targets of analyzed lncRNAs
  578. ```{r lnc_RNARNA_scanner: analyze_onlyDEtargets_FALSE, echo=FALSE }
  579. utils::data(lnc_RNARNA_scanner_output, package = 'wizbionet')
  580. output<-lnc_RNARNA_scanner_output$lncRNA_AllTargets
  581. output<-head(output,n=10)
  582. rownames(output)<-NULL
  583. #names(output)
  584. #table
  585. temp<- knitr::kable(output, align="l")
  586. temp<- kableExtra::kable_styling(temp,font_size=8, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", stripe_color = "white")
  587. temp<-kableExtra::column_spec(temp, column=c(10), color="red")
  588. temp<- kableExtra::scroll_box(temp, width = "100%", height = "200px")
  590. temp
  592. ```
  593. ##### **C. additionally output always has a list "result_lnc_RNAtoRNA_list"**
  594. This list has complete results (including binding sites, location, rank) for each of the analyzed transcripts.
  595. Each transcript has assigned two sub-lists "ENSTnumber_minEnergy" and "ENSTnumber_sumEnergy"
  596. which were used for selection of the top targets. Those dataframes are a raw otput from RNARNA database.
  598. ```{r lnc_RNARNA_scanner: result_lnc_RNAtoRNA_list, echo=FALSE }
  599. utils::data(lnc_RNARNA_scanner_output, package = 'wizbionet')
  600. output<-lnc_RNARNA_scanner_output$result_lnc_RNAtoRNA_list$ENST00000436290_minEnergy
  601. output<-head(output,n=10)
  602. rownames(output)<-NULL
  603. #names(output)
  604. #table
  605. temp<- knitr::kable(output, align="l")
  606. temp<- kableExtra::kable_styling(temp,font_size=8, bootstrap_options = c('condensed'), latex_options=c('scale_down'),full_width = FALSE, position = "left", stripe_color = "white")
  607. temp<-kableExtra::column_spec(temp, column=c(4,5))
  608. temp<- kableExtra::scroll_box(temp, width = "100%", height = "200px")
  610. temp
  612. ```
  615. ### 13. Copying gene list to the clipboard with venny.list()
  617. If you want to quickly check the overlaps betwen the gene lists you can utilize function venny.list()
  618. it will copy to clipboard the vector of characters, remove NAs and duplicates. Additionally it will print the number of collected items. This function was created to use together with the webpage http://bioinfogp.cnb.csic.es/tools/venny/ or paste results to the excel or cytoscape
  620. ```{r eval=FALSE }
  621. genes<-c('SGCD', 'TBC1D8B', 'PTGER3', 'ANO5', 'FGFR1', 'PTGFR','SNTB2', 'USP9Y', 'PTPRM', 'CRIM1', 'BRD1', 'RAD23B', 'PRKDC')
  622. counts<- c(66, 62, 54, 40, 34, 32, 32, 16, 15, 15, 15, 14,10)
  623. input<- data.frame(genes,counts)
  625. wizbionet::venny.list(input$genes)
  627. ```
  632. ## New functions added in wizbionet 1.3.0
  634. The functions below were added in the 1.3.0 update. Examples use small gene / miRNA data frames; set `eval=TRUE` to run them.
  636. ### 14. Updating gene symbols with the 2023 annotation using NCBI_synonyms2023()
  638. Same behaviour as **NCBI_synonyms()** but based on the June 2023 NCBI annotation bundled with the package (`inst/extdata/Homo_sapiens_uncoll_Jun2023.txt.gz`). You can also supply your own annotation table through the `annotation` argument.
  640. ```{r, eval=FALSE }
  641. genelist <- data.frame(symbols = c('EP3', 'SNT3', 'TP53', 'ONECUT2'))
  642. NCBI_synonyms2023(genelist, "symbols")
  643. ```
  645. ### 15. Unifying symbols across several lists using unify_symbols()
  647. Maps every column of gene symbols to official NCBI symbols and binds the unified lists side by side. Choose the 2020 (default) or 2023 annotation.
  649. ```{r, eval=FALSE }
  650. gene_lists <- data.frame(
  651. listGWAS = c('EP3', 'SNT3', 'TP53'),
  652. listGO = c('PTGER3', 'MYC', 'KRAS'))
  653. unify_symbols(gene_lists) # 2020 annotation (default)
  654. unify_symbols(gene_lists, use_2023 = TRUE) # 2023 annotation
  655. ```
  657. ### 16. Collapsing oversized cells using coll_reduction()
  659. Replaces long collapsed gene strings with a `"more than <cutoff>"` label wherever the paired `_COUNT` column exceeds the cutoff. Keeps wide enrichment tables readable.
  661. ```{r, eval=FALSE }
  662. df <- data.frame(
  663. Term = c('Oxidative phosphorylation WP623', 'Apoptosis WP254'),
  664. genes_coll = c('ATP5F1A;NDUFS2;NDUFS1;NDUFV2;NDUFV1', 'BID;BBC3'),
  665. genes_COUNT = c(45, 2))
  666. coll_reduction(df, cutoff = 30)
  667. ```
  669. ### 17. Counting genes overlapping a key vector using count_keyvec()
  671. For each column of separated gene strings, keeps only the genes present in a key vector and reports the overlap count per row.
  673. ```{r, eval=FALSE }
  674. df <- data.frame(
  675. pathway = c('apoptosis', 'MAPK'),
  676. genes = c('TP53;EGFR;MYC', 'KRAS;BRAF'))
  677. count_keyvec(df, cols = 'genes', separator = ';',
  678. genes_vec = c('TP53', 'MYC', 'KRAS'))
  679. ```
  681. ### 18. Reverse-combining logical columns into gene lists using reverse_list_comb()
  683. Extracts the identifiers flagged `TRUE` in each logical column and returns one gene list per column.
  685. ```{r, eval=FALSE }
  686. df <- data.frame(
  687. SymbolNCBI = c('TP53', 'EGFR', 'MYC'),
  688. Hippocampus = c(TRUE, FALSE, TRUE),
  689. Cortex = c(FALSE, FALSE, TRUE))
  690. reverse_list_comb(df, main_col = 'SymbolNCBI')
  691. ```
  693. For a non-gene identifier column (e.g. metabolites) use **rev_lists_comb_other()** with `input_col=`.
  695. ### 19. Aggregating miRNA-target tables across tissues using col_agrecounter_log()
  697. Extends **col_agrecounter()** with a loop over several logical membership columns (e.g. tissues), aggregating and counting targets per key.
  699. ```{r, eval=FALSE }
  700. df <- data.frame(
  701. pre_miRNA = c('hsa-miR-195', 'hsa-miR-195', 'hsa-miR-4753'),
  702. Target = c('CDH24', 'PAX1', 'TGFB3'),
  703. Hippocampus_log = c(TRUE, TRUE, TRUE),
  704. Cortex_log = c(TRUE, FALSE, TRUE))
  705. col_agrecounter_log(df, col_names = 'Target', col_collapse = 'pre_miRNA',
  706. cols_log = c('Hippocampus_log', 'Cortex_log'))
  707. ```
  709. ### 20. Expanding miRNA names using miR_mature()
  711. Splits each miRNA into precursor and mature (`-5p`, `-3p`) query rows for multiMiR.
  713. ```{r, eval=FALSE }
  714. df <- data.frame(miRNA = c('hsa-mir-21', 'hsa-miR-155-5p'))
  715. miR_mature(df, col = 'miRNA')
  716. ```
  718. ### 21. Small helpers: countgenes() and log_to_NR()
  720. ```{r, eval=FALSE }
  721. # encode the number of unique genes into the column names
  722. countgenes(data.frame(listA = c('TP53', 'EGFR', 'TP53'),
  723. listB = c('MYC', 'KRAS', 'BRAF')))
  725. # convert a TRUE/FALSE membership matrix to 1/0
  726. log_to_NR(data.frame(setA = c(TRUE, FALSE), setB = c(FALSE, TRUE)))
  727. ```
  730. ## How to cite
  732. If you use **wizbionet** in your research, please cite the primary article:
  734. > Wicik Z, Jales Neto LH, Guzman LEF, Pavao R, Takayama L, Caparbo VF, Lopes NHM, Pereira AC, Pereira RMR (2021). *The crosstalk between bone metabolism, lncRNAs, microRNAs and mRNAs in coronary artery calcification.* Genomics 113(1 Pt 2):503-513. doi: [10.1016/j.ygeno.2020.09.041](https://doi.org/10.1016/j.ygeno.2020.09.041) (PMID: 32971215).
  736. You can also retrieve this from R with `citation("wizbionet")`.
  738. Some functions rely on external tools and databases; when you use them, please also cite:
  740. - **topmiRNA_toptarget()** uses the multiMiR package and database: Ru Y, Kechris KJ, Tabakoff B, et al. (2014). *The multiMiR R package and database: integration of microRNA-target interactions along with their disease and drug associations.* Nucleic Acids Research 42(17):e133. doi: [10.1093/nar/gku631](https://doi.org/10.1093/nar/gku631).
  741. - **lnc_RNARNA_scanner()** mines the RNARNA database: Terai G, Iwakiri J, Kameda T, Hamada M, Asai K (2016). *Comprehensive prediction of lncRNA-RNA interactions in human transcriptome.* BMC Genomics 17(S1):12. doi: [10.1186/s12864-015-2307-5](https://doi.org/10.1186/s12864-015-2307-5).

vignette_wizbionet.Rmd at commit 6f77972, under MIT · at the source

Overview

Authors: Janusz Szyndler1, Zofia Wicik2, Anna Wierucka3, Piotr Maciejak2, Michał Sobstyl4, Angelika Stapińska-Syniec4, Piotr Glinka4, Karol Piwowarski4, Natalia Chmielewska2
ORCID iDs: Janusz Szyndler, Angelika Stapińska-Syniec
  1. Department of Experimental and Clinical Pharmacology, Center for Preclinical Research and Technology CePT, Medical University of Warsaw, Banacha 1B Street, 02-097 Warsaw, Poland
  2. Department of Experimental and Clinical Neuroscience, Institute of Psychiatry and Neurology, Sobieskiego 9 Street, 02-957 Warsaw, Poland; (Z.W.); (P.M.)
  3. Faculty of Medicine, Medical University of Warsaw, Zwirki and Wigury 61 Street, 02-097 Warsaw, Poland
  4. Department of Neurosurgery, Institute of Psychiatry and Neurology, Warsaw, Poland, Sobieskiego 9 Street 02-957 Warsaw, Poland; (M.S.); (A.S.-S.); (P.G.); (K.P.)
Institutions: Medical University of Warsaw (Poland); Institute of Psychiatry and Neurology (Poland)
Journal: Brain sciences, volume 16, issue 4, article 347
Dates: received 17 February 2026; accepted 20 March 2026; published online 24 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/brainsci16040347 · PMID 42041758 · PMCID PMC13115420 · OpenAlex W7140281210
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: other (modality), human (organism), other condition (population), Parkinson's (population), cellular / molecular (subfield)
Methods: Statistics
Keywords: Parkinson’s disease, gliomas, miR-16-5p, miR-32-5p, p53
Topic: MicroRNA in disease regulation (Cancer Research, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: This study was funded by internal resources of the Department of Experimental and Clinical Pharmacology, Centre for Preclinical Research and Technology CePT, Medical University of Warsaw, and the Department of Experimental and Clinical Neuroscience, Insti (none)
Citations: not cited yet (Europe PMC); 52 references in the paper

Abstract

Highlights: What are the main findings?

A 953 of genes was shared between glioblastoma and Parkinson’s disease (PD), indicating substantial overlap in disease-related molecular pathways.

Plasma levels of miR-16-5p and miR-32-5p were significantly higher in glioma patients compared with PD patients and controls, and miR-16-5p together with p53 were increased in glioblastoma tumor tissue.

What are the implications of the main findings?

miR-16-5p and miR-32-5p may serve as peripheral biomarkers associated with glioma-related molecular pathways.

The shared gene network indicates a potential molecular connection between neurodegeneration and glioma biology. Further studies are needed to identify specific pathways involved in both cell death and proliferation.

Abstract: Objectives: In this study, we explore the molecular basis of the literature-reported inverse association between brain neoplasms and neurodegenerative disorders, including Parkinson’s disease (PD). As miRNAs are post-transcriptional regulators, we selected them as candidates underlying opposite processes of neurodegeneration and glioma development. Methods: We used bioinformatic analyses for disease-gene extraction, miRNA target prediction, enrichment analyses, and miRNA ranking. We identified 953 shared genes between PD and glioblastoma (GBM) in DisGeNET, then prioritized miRNAs predicted to regulate the largest number of shared targets. Next, we collected peripheral blood from patients with PD (n = 12), patients with gliomas (the most advanced—grade IV, n = 10 and grade III n = 3) and controls undergoing spinal surgery for disk pathology (n = 10). Blood samples were obtained pre-operatively in controls and glioma patients. Tumor and peritumoral tissues were obtained from glioma patients, whereas tissue sampling is not feasible in PD. Brain tissues and plasma were analyzed using RT-qPCR (miRNA) and ELISA (p53). Results: We observed increased levels of miR-16-5p (p < 0.05) and p53 protein (p < 0.05) in tumor tissues compared with peritumoral tissue. Additionally, miR-16-5p and miR-32-5p plasma levels were elevated in glioma patients compared with both PD patients (p < 0.01 and p < 0.001, respectively) and controls (p < 0.01 and p < 0.001, respectively). Plasma levels in PD did not differ from controls. Conclusions: Although these analyses highlight miR-16-5p and miR-32-5p as candidate biomarkers associated with glioma related pathways, the results did not provide evidence for the expected opposite regulation between PD and glioma. Future studies with a larger cohort of patients using high-throughput methods are needed to validate these findings and to elucidate the mechanisms driving neurodegeneration or excessive proliferation.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

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wizbionet/wizbionet

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 6f779729014c54ec96a5efc1afb6e997af543d06, 21 July 2026
Languages: R (39)
Size: 87 files, 39 scripts
Software Heritage: archived
Found in: the text, “2.1. Bioinformatic Assessment”
Holds: README, license file, environment (DESCRIPTION), tests, continuous integration, documentation, 3 notebooks
Not found: CITATION.cff
Tools: tidyverse (10 files), data.table (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
41 files
At the source: github.com/wizbionet/wizbionet/blob/master/doc/vignette_wizbionet.md

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Data Availability Statement

Data supporting the findings of this study are available from the corresponding author upon request. The results of the bioinformatic analyses are provided in Supplementary Table S1.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 5 keywords, 1 funder, 52 references.

Cite

This paper

Szyndler, J., Wicik, Z., Wierucka, A., Maciejak, P., Sobstyl, M., Stapińska-Syniec, A., Glinka, P., Piwowarski, K., & Chmielewska, N. (2026). In Silico-Identified miR-16-5p and miR-32-5p as a Shared Molecular Signature of Primary Gliomas and Parkinson's Disease: Plasma Levels Are Increased Only in Glioma Patients. Brain sciences, 16(4), 347. https://doi.org/10.3390/brainsci16040347

BibTeX

@article{szyndler2026silico,
author = {Szyndler, Janusz and Wicik, Zofia and Wierucka, Anna and Maciejak, Piotr and Sobstyl, Michał and Stapińska-Syniec, Angelika and Glinka, Piotr and Piwowarski, Karol and Chmielewska, Natalia},
title = {{In Silico-Identified miR-16-5p and miR-32-5p as a Shared Molecular Signature of Primary Gliomas and Parkinson's Disease: Plasma Levels Are Increased Only in Glioma Patients}},
journal = {Brain sciences},
year = {2026},
month = mar,
volume = {16},
number = {4},
pages = {347},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2076-3425},
doi = {10.3390/brainsci16040347},
url = {https://doi.org/10.3390/brainsci16040347},
pmid = {42041758},
pmcid = {PMC13115420}
}

RIS

TY - JOUR
AU - Szyndler, Janusz
AU - Wicik, Zofia
AU - Wierucka, Anna
AU - Maciejak, Piotr
AU - Sobstyl, Michał
AU - Stapińska-Syniec, Angelika
AU - Glinka, Piotr
AU - Piwowarski, Karol
AU - Chmielewska, Natalia
TI - In Silico-Identified miR-16-5p and miR-32-5p as a Shared Molecular Signature of Primary Gliomas and Parkinson's Disease: Plasma Levels Are Increased Only in Glioma Patients
T2 - Brain sciences
J2 - Brain Sci
PY - 2026
DA - 2026/03/24
VL - 16
IS - 4
SP - 347
SN - 2076-3425
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/brainsci16040347
UR - https://doi.org/10.3390/brainsci16040347
LA - en
ER -

CSL-JSON

{
"id": "10.3390/brainsci16040347",
"type": "article-journal",
"title": "In Silico-Identified miR-16-5p and miR-32-5p as a Shared Molecular Signature of Primary Gliomas and Parkinson's Disease: Plasma Levels Are Increased Only in Glioma Patients",
"container-title": "Brain sciences",
"author": [
{
"family": "Szyndler",
"given": "Janusz"
},
{
"family": "Wicik",
"given": "Zofia"
},
{
"family": "Wierucka",
"given": "Anna"
},
{
"family": "Maciejak",
"given": "Piotr"
},
{
"family": "Sobstyl",
"given": "Michał"
},
{
"family": "Stapińska-Syniec",
"given": "Angelika"
},
{
"family": "Glinka",
"given": "Piotr"
},
{
"family": "Piwowarski",
"given": "Karol"
},
{
"family": "Chmielewska",
"given": "Natalia"
}
],
"container-title-short": "Brain Sci",
"volume": "16",
"issue": "4",
"page": "347",
"DOI": "10.3390/brainsci16040347",
"PMID": "42041758",
"PMCID": "PMC13115420",
"ISSN": "2076-3425",
"publisher": "Multidisciplinary Digital Publishing Institute (MDPI)",
"URL": "https://doi.org/10.3390/brainsci16040347",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
24
]
]
}
}

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